Automotive airbag safety and effectiveness increasingly demand an occupant sensing system that can accurately classify the occupant types and positions. There are two types of known classification processes referred as “static classification” and “dynamic classification.” The static classification typically requires an update speed of a few seconds within which a change of occupant type (for example, an occupant is leaving or entering the vehicle) may occur. When a person is already present in the seat, however, dynamic classification is desirable since that position may change quickly during an accident from hard braking or impact. It is generally known that the required dynamic response time should be in the order of a few tens of milliseconds.
Depending on specific applications, an occupant in the front passenger seat may be classified into several categories such as adult, child without car seat (Child), rear-facing infant seat (RIFS), forward facing child seat (FFCS), and empty seat (ES). The function of a dynamic classifier is to further identify the relative position of a person (adult/child) within an established category. If the person is inside a predetermined at-risk zone (i.e. head and/or torso is too close to the airbag), the occupant should be identified as Out-of Position (OOP). On the other hand, if the person is outside the at-risk zone, the occupant should be identified as normal position (NP). The dynamic classifier functions are further identified in U.S. patent application Ser. No. 11/218,671, titled Vision-Based Occupant Classification Method and System for Controlling Airbag Deployment in a Vehicle Restraint System, Filed Sep. 2, 2005, (DP-313789) assigned to Delphi Technologies, Inc., and incorporated herein in it's entirety.
Challenges to achieve vision based dynamic classifications include required accuracy and speed. Complex image features and algorithms are usually needed to obtain robust and accurate classification, which at the same time, fails to meet the required speed. In some cases, occupant head/torso tracking algorithms are used for dynamic classification. A cascaded classification structure that allows a prior knowledge of the occupant type to be established through static classification would be desirable for speed considerations. A motion-detection based state machine could then be used to safeguard the status or prior classification type if no change in type has occurred so that a proper dynamic classification process can be applied. However, an effective dynamic classifier remains to be developed. Furthermore, it has been observed that the motion-detection based state machine could experience false detections under certain conditions. For example, when an occupant exits/enters the vehicle slowly, the event may not be detected (false positive). Or a false detection by the state machine may occur when an occupant moves inside the vehicle in a certain way (false negative).